Overview

Brought to you by YData

Dataset statistics

Number of variables43
Number of observations798
Missing cells1457
Missing cells (%)4.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory268.2 KiB
Average record size in memory344.2 B

Variable types

Text1
Numeric14
Categorical28

Alerts

bluetooth has constant value "1"Constant
os is highly imbalanced (80.1%)Imbalance
warranty is highly imbalanced (63.7%)Imbalance
aspect_ratio is highly imbalanced (56.3%)Imbalance
touch_screen is highly imbalanced (58.8%)Imbalance
hdd is highly imbalanced (91.9%)Imbalance
hdmi is highly imbalanced (68.7%)Imbalance
wifi is highly imbalanced (96.4%)Imbalance
inbuilt_microphone is highly imbalanced (90.3%)Imbalance
display_port is highly imbalanced (86.5%)Imbalance
type_c is highly imbalanced (83.8%)Imbalance
processor_brand is highly imbalanced (53.7%)Imbalance
thickness has 185 (23.2%) missing valuesMissing
weight has 78 (9.8%) missing valuesMissing
cache has 27 (3.4%) missing valuesMissing
thread has 20 (2.5%) missing valuesMissing
camera has 769 (96.4%) missing valuesMissing
num_of_cell has 159 (19.9%) missing valuesMissing
battery_capacity has 202 (25.3%) missing valuesMissing
name has unique valuesUnique

Reproduction

Analysis started2024-08-17 18:29:31.424434
Analysis finished2024-08-17 18:30:31.082761
Duration59.66 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

name
Text

UNIQUE 

Distinct798
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:31.459374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length127
Median length99
Mean length86.833333
Min length37

Characters and Unicode

Total characters69293
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique798 ?
Unique (%)100.0%

Sample

1st rowAcer One 14 Z8-415 Laptop (11th Gen Core i3 / 8GB/ 512GB SSD/ Win11 Home)
2nd rowWings Nuvobook V1 Laptop (11th Gen Core i5/ 8GB/ 512GB SSD/ Win11)
3rd rowMSI Thin GF63 12HW-012IN Gaming Laptop (12th Gen Core i5/ 16GB/ 512GB SSD/ Win11 Home/ 4GB Graphics)
4th rowAcer Nitro V ANV15-51 Gaming Laptop (13th Gen Core i5/ 8GB/ 512GB SSD/ Win11/ 6GB Graph)
5th rowAcer Aspire Lite AL15-51 Laptop (AMD Ryzen 5 5500U/ 16GB/ 512GB SSD/ Win11)
ValueCountFrequency (%)
laptop 790
 
6.6%
ssd 764
 
6.4%
win11 665
 
5.6%
core 557
 
4.7%
gen 555
 
4.6%
512gb 525
 
4.4%
16gb 416
 
3.5%
home 379
 
3.2%
8gb 348
 
2.9%
graph 285
 
2.4%
Other values (1095) 6680
55.8%
2024-08-17T18:30:32.289606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11166
 
16.1%
1 4454
 
6.4%
G 3145
 
4.5%
o 2944
 
4.2%
/ 2710
 
3.9%
e 2483
 
3.6%
n 2303
 
3.3%
i 2197
 
3.2%
B 2112
 
3.0%
S 2091
 
3.0%
Other values (58) 33688
48.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11166
 
16.1%
1 4454
 
6.4%
G 3145
 
4.5%
o 2944
 
4.2%
/ 2710
 
3.9%
e 2483
 
3.6%
n 2303
 
3.3%
i 2197
 
3.2%
B 2112
 
3.0%
S 2091
 
3.0%
Other values (58) 33688
48.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11166
 
16.1%
1 4454
 
6.4%
G 3145
 
4.5%
o 2944
 
4.2%
/ 2710
 
3.9%
e 2483
 
3.6%
n 2303
 
3.3%
i 2197
 
3.2%
B 2112
 
3.0%
S 2091
 
3.0%
Other values (58) 33688
48.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11166
 
16.1%
1 4454
 
6.4%
G 3145
 
4.5%
o 2944
 
4.2%
/ 2710
 
3.9%
e 2483
 
3.6%
n 2303
 
3.3%
i 2197
 
3.2%
B 2112
 
3.0%
S 2091
 
3.0%
Other values (58) 33688
48.6%

price
Real number (ℝ)

Distinct395
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85206.959
Minimum11990
Maximum569990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:32.598894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11990
5-th percentile26990
Q147990
median65745
Q394990
95-th percentile210171.35
Maximum569990
Range558000
Interquartile range (IQR)47000

Descriptive statistics

Standard deviation66340.745
Coefficient of variation (CV)0.77858366
Kurtosis10.531844
Mean85206.959
Median Absolute Deviation (MAD)22020
Skewness2.8190422
Sum67995153
Variance4.4010945 × 109
MonotonicityNot monotonic
2024-08-17T18:30:32.877254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59990 17
 
2.1%
54990 14
 
1.8%
62990 14
 
1.8%
49990 12
 
1.5%
69990 12
 
1.5%
58990 11
 
1.4%
36990 11
 
1.4%
109990 11
 
1.4%
79990 11
 
1.4%
44990 11
 
1.4%
Other values (385) 674
84.5%
ValueCountFrequency (%)
11990 2
0.3%
12989 1
 
0.1%
12990 1
 
0.1%
13990 1
 
0.1%
15990 2
0.3%
16990 4
0.5%
18990 3
0.4%
19990 1
 
0.1%
20890 1
 
0.1%
20990 2
0.3%
ValueCountFrequency (%)
569990 1
0.1%
453490 1
0.1%
446390 1
0.1%
429990 1
0.1%
415000 1
0.1%
399999 1
0.1%
390914 1
0.1%
379990 1
0.1%
367590 1
0.1%
354490 1
0.1%

brand
Categorical

Distinct27
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
Asus
164 
HP
156 
Lenovo
138 
MSI
88 
Dell
86 
Other values (22)
166 

Length

Max length9
Median length8
Mean length4.0601504
Min length2

Characters and Unicode

Total characters3240
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.6%

Sample

1st rowAcer
2nd rowWings
3rd rowMSI
4th rowAcer
5th rowAcer

Common Values

ValueCountFrequency (%)
Asus 164
20.6%
HP 156
19.5%
Lenovo 138
17.3%
MSI 88
11.0%
Dell 86
10.8%
Acer 73
9.1%
Infinix 17
 
2.1%
Samsung 11
 
1.4%
Apple 10
 
1.3%
LG 10
 
1.3%
Other values (17) 45
 
5.6%

Length

2024-08-17T18:30:33.195835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
asus 165
20.7%
hp 156
19.5%
lenovo 138
17.3%
msi 88
11.0%
dell 86
10.8%
acer 73
9.1%
infinix 17
 
2.1%
samsung 11
 
1.4%
apple 10
 
1.3%
lg 10
 
1.3%
Other values (16) 44
 
5.5%

Most occurring characters

ValueCountFrequency (%)
s 351
 
10.8%
e 326
 
10.1%
o 293
 
9.0%
A 252
 
7.8%
u 193
 
6.0%
n 192
 
5.9%
l 187
 
5.8%
H 162
 
5.0%
P 158
 
4.9%
L 150
 
4.6%
Other values (31) 976
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 351
 
10.8%
e 326
 
10.1%
o 293
 
9.0%
A 252
 
7.8%
u 193
 
6.0%
n 192
 
5.9%
l 187
 
5.8%
H 162
 
5.0%
P 158
 
4.9%
L 150
 
4.6%
Other values (31) 976
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 351
 
10.8%
e 326
 
10.1%
o 293
 
9.0%
A 252
 
7.8%
u 193
 
6.0%
n 192
 
5.9%
l 187
 
5.8%
H 162
 
5.0%
P 158
 
4.9%
L 150
 
4.6%
Other values (31) 976
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 351
 
10.8%
e 326
 
10.1%
o 293
 
9.0%
A 252
 
7.8%
u 193
 
6.0%
n 192
 
5.9%
l 187
 
5.8%
H 162
 
5.0%
P 158
 
4.9%
L 150
 
4.6%
Other values (31) 976
30.1%

no_of_votes
Real number (ℝ)

Distinct242
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284.86717
Minimum51
Maximum14917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:33.464240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile56
Q174
median94
Q3122
95-th percentile857.65
Maximum14917
Range14866
Interquartile range (IQR)48

Descriptive statistics

Standard deviation1086.8141
Coefficient of variation (CV)3.8151609
Kurtosis130.38878
Mean284.86717
Median Absolute Deviation (MAD)22
Skewness10.772538
Sum227324
Variance1181164.9
MonotonicityNot monotonic
2024-08-17T18:30:33.753491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 18
 
2.3%
83 16
 
2.0%
110 15
 
1.9%
101 15
 
1.9%
106 15
 
1.9%
85 14
 
1.8%
58 14
 
1.8%
69 14
 
1.8%
94 14
 
1.8%
81 13
 
1.6%
Other values (232) 650
81.5%
ValueCountFrequency (%)
51 8
1.0%
52 7
0.9%
53 7
0.9%
54 8
1.0%
55 8
1.0%
56 11
1.4%
57 6
0.8%
58 14
1.8%
59 10
1.3%
60 10
1.3%
ValueCountFrequency (%)
14917 1
0.1%
14846 1
0.1%
13399 1
0.1%
12724 1
0.1%
6907 1
0.1%
4833 1
0.1%
4046 1
0.1%
3129 1
0.1%
3050 1
0.1%
2964 1
0.1%

rating
Real number (ℝ)

Distinct24
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3323308
Minimum3.55
Maximum4.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:34.024975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.55
5-th percentile4
Q14.15
median4.3
Q34.5
95-th percentile4.7
Maximum4.75
Range1.2
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.23033951
Coefficient of variation (CV)0.053167573
Kurtosis-0.67660852
Mean4.3323308
Median Absolute Deviation (MAD)0.2
Skewness0.06418695
Sum3457.2
Variance0.053056292
MonotonicityNot monotonic
2024-08-17T18:30:34.306144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4.3 75
 
9.4%
4.2 72
 
9.0%
4.1 63
 
7.9%
4.15 57
 
7.1%
4.4 54
 
6.8%
4.25 51
 
6.4%
4.35 47
 
5.9%
4 46
 
5.8%
4.6 45
 
5.6%
4.5 42
 
5.3%
Other values (14) 246
30.8%
ValueCountFrequency (%)
3.55 1
 
0.1%
3.65 2
 
0.3%
3.7 1
 
0.1%
3.75 1
 
0.1%
3.8 1
 
0.1%
3.85 3
 
0.4%
3.9 2
 
0.3%
3.95 9
 
1.1%
4 46
5.8%
4.05 39
4.9%
ValueCountFrequency (%)
4.75 37
4.6%
4.7 37
4.6%
4.65 36
4.5%
4.6 45
5.6%
4.55 40
5.0%
4.5 42
5.3%
4.45 37
4.6%
4.4 54
6.8%
4.35 47
5.9%
4.3 75
9.4%

os
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
Windows
760 
Others
 
28
Mac
 
10

Length

Max length7
Median length7
Mean length6.914787
Min length3

Characters and Unicode

Total characters5518
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWindows
2nd rowWindows
3rd rowWindows
4th rowWindows
5th rowWindows

Common Values

ValueCountFrequency (%)
Windows 760
95.2%
Others 28
 
3.5%
Mac 10
 
1.3%

Length

2024-08-17T18:30:34.574352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:34.879588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
windows 760
95.2%
others 28
 
3.5%
mac 10
 
1.3%

Most occurring characters

ValueCountFrequency (%)
s 788
14.3%
W 760
13.8%
i 760
13.8%
n 760
13.8%
d 760
13.8%
o 760
13.8%
w 760
13.8%
O 28
 
0.5%
t 28
 
0.5%
h 28
 
0.5%
Other values (5) 86
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 788
14.3%
W 760
13.8%
i 760
13.8%
n 760
13.8%
d 760
13.8%
o 760
13.8%
w 760
13.8%
O 28
 
0.5%
t 28
 
0.5%
h 28
 
0.5%
Other values (5) 86
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 788
14.3%
W 760
13.8%
i 760
13.8%
n 760
13.8%
d 760
13.8%
o 760
13.8%
w 760
13.8%
O 28
 
0.5%
t 28
 
0.5%
h 28
 
0.5%
Other values (5) 86
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 788
14.3%
W 760
13.8%
i 760
13.8%
n 760
13.8%
d 760
13.8%
o 760
13.8%
w 760
13.8%
O 28
 
0.5%
t 28
 
0.5%
h 28
 
0.5%
Other values (5) 86
 
1.6%

utility
Categorical

Distinct4
Distinct (%)0.5%
Missing6
Missing (%)0.8%
Memory size6.4 KiB
Performance
352 
Everyday Use
208 
Gaming
173 
Business
59 

Length

Max length12
Median length11
Mean length9.9469697
Min length6

Characters and Unicode

Total characters7878
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEveryday Use
2nd rowBusiness
3rd rowPerformance
4th rowGaming
5th rowPerformance

Common Values

ValueCountFrequency (%)
Performance 352
44.1%
Everyday Use 208
26.1%
Gaming 173
21.7%
Business 59
 
7.4%
(Missing) 6
 
0.8%

Length

2024-08-17T18:30:35.133967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:35.411844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
performance 352
35.2%
everyday 208
20.8%
use 208
20.8%
gaming 173
17.3%
business 59
 
5.9%

Most occurring characters

ValueCountFrequency (%)
e 1179
15.0%
r 912
11.6%
a 733
 
9.3%
n 584
 
7.4%
m 525
 
6.7%
y 416
 
5.3%
s 385
 
4.9%
P 352
 
4.5%
f 352
 
4.5%
o 352
 
4.5%
Other values (11) 2088
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1179
15.0%
r 912
11.6%
a 733
 
9.3%
n 584
 
7.4%
m 525
 
6.7%
y 416
 
5.3%
s 385
 
4.9%
P 352
 
4.5%
f 352
 
4.5%
o 352
 
4.5%
Other values (11) 2088
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1179
15.0%
r 912
11.6%
a 733
 
9.3%
n 584
 
7.4%
m 525
 
6.7%
y 416
 
5.3%
s 385
 
4.9%
P 352
 
4.5%
f 352
 
4.5%
o 352
 
4.5%
Other values (11) 2088
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1179
15.0%
r 912
11.6%
a 733
 
9.3%
n 584
 
7.4%
m 525
 
6.7%
y 416
 
5.3%
s 385
 
4.9%
P 352
 
4.5%
f 352
 
4.5%
o 352
 
4.5%
Other values (11) 2088
26.5%

thickness
Real number (ℝ)

MISSING 

Distinct110
Distinct (%)17.9%
Missing185
Missing (%)23.2%
Infinite0
Infinite (%)0.0%
Mean26.717129
Minimum1.95
Maximum376.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:35.672799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.95
5-th percentile15.1
Q117.9
median19.8
Q322.5
95-th percentile27.2
Maximum376.17
Range374.22
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation40.308621
Coefficient of variation (CV)1.5087183
Kurtosis32.443346
Mean26.717129
Median Absolute Deviation (MAD)1.9
Skewness5.7276824
Sum16377.6
Variance1624.7849
MonotonicityNot monotonic
2024-08-17T18:30:35.963271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.9 97
 
12.2%
17.9 78
 
9.8%
18.9 28
 
3.5%
24.9 17
 
2.1%
19 17
 
2.1%
23.5 16
 
2.0%
17 16
 
2.0%
21.7 15
 
1.9%
26.9 13
 
1.6%
16.9 12
 
1.5%
Other values (100) 304
38.1%
(Missing) 185
23.2%
ValueCountFrequency (%)
1.95 1
 
0.1%
2.6 4
0.5%
9 1
 
0.1%
10.9 4
0.5%
11.3 1
 
0.1%
11.5 1
 
0.1%
11.65 1
 
0.1%
12.8 1
 
0.1%
12.9 1
 
0.1%
12.95 1
 
0.1%
ValueCountFrequency (%)
376.17 1
 
0.1%
277.33 1
 
0.1%
263.8 1
 
0.1%
259.4 1
 
0.1%
259 4
0.5%
249.1 2
0.3%
242 3
0.4%
240 1
 
0.1%
234.3 3
0.4%
211.1 1
 
0.1%

weight
Real number (ℝ)

MISSING 

Distinct105
Distinct (%)14.6%
Missing78
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean1.8346806
Minimum1
Maximum3.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:36.267916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.3
Q11.57
median1.74
Q32.14
95-th percentile2.6
Maximum3.86
Range2.86
Interquartile range (IQR)0.57

Descriptive statistics

Standard deviation0.41023709
Coefficient of variation (CV)0.22360137
Kurtosis0.82450162
Mean1.8346806
Median Absolute Deviation (MAD)0.24
Skewness0.92021243
Sum1320.97
Variance0.16829447
MonotonicityNot monotonic
2024-08-17T18:30:36.547580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8 55
 
6.9%
1.7 54
 
6.8%
2.25 33
 
4.1%
1.86 26
 
3.3%
1.4 26
 
3.3%
1.5 25
 
3.1%
1.69 24
 
3.0%
1.6 21
 
2.6%
1.65 20
 
2.5%
1.75 17
 
2.1%
Other values (95) 419
52.5%
(Missing) 78
 
9.8%
ValueCountFrequency (%)
1 3
 
0.4%
1.16 1
 
0.1%
1.18 1
 
0.1%
1.19 2
 
0.3%
1.2 4
 
0.5%
1.21 1
 
0.1%
1.23 2
 
0.3%
1.24 11
1.4%
1.25 4
 
0.5%
1.29 4
 
0.5%
ValueCountFrequency (%)
3.86 1
 
0.1%
3.3 1
 
0.1%
3.25 1
 
0.1%
3.23 2
 
0.3%
3 2
 
0.3%
2.87 2
 
0.3%
2.8 4
0.5%
2.77 1
 
0.1%
2.72 4
0.5%
2.7 7
0.9%

warranty
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing5
Missing (%)0.6%
Memory size6.4 KiB
1.0
700 
2.0
84 
3.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2379
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 700
87.7%
2.0 84
 
10.5%
3.0 9
 
1.1%
(Missing) 5
 
0.6%

Length

2024-08-17T18:30:36.824601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:37.068220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 700
88.3%
2.0 84
 
10.6%
3.0 9
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 793
33.3%
0 793
33.3%
1 700
29.4%
2 84
 
3.5%
3 9
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 793
33.3%
0 793
33.3%
1 700
29.4%
2 84
 
3.5%
3 9
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 793
33.3%
0 793
33.3%
1 700
29.4%
2 84
 
3.5%
3 9
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 793
33.3%
0 793
33.3%
1 700
29.4%
2 84
 
3.5%
3 9
 
0.4%

display_size
Real number (ℝ)

Distinct20
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.191805
Minimum11.6
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:37.307617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11.6
5-th percentile14
Q114
median15.6
Q315.6
95-th percentile16.1
Maximum18
Range6.4
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation0.9453087
Coefficient of variation (CV)0.062224912
Kurtosis0.61726174
Mean15.191805
Median Absolute Deviation (MAD)0
Skewness-0.72980231
Sum12123.06
Variance0.89360853
MonotonicityNot monotonic
2024-08-17T18:30:37.533207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
15.6 416
52.1%
14 195
24.4%
16 100
 
12.5%
13.3 21
 
2.6%
16.1 17
 
2.1%
17.3 14
 
1.8%
17 7
 
0.9%
11.6 6
 
0.8%
14.1 5
 
0.6%
13.4 4
 
0.5%
Other values (10) 13
 
1.6%
ValueCountFrequency (%)
11.6 6
 
0.8%
13 1
 
0.1%
13.3 21
 
2.6%
13.4 4
 
0.5%
13.5 1
 
0.1%
13.6 1
 
0.1%
14 195
24.4%
14.1 5
 
0.6%
14.2 2
 
0.3%
14.5 2
 
0.3%
ValueCountFrequency (%)
18 2
 
0.3%
17.3 14
 
1.8%
17 7
 
0.9%
16.2 1
 
0.1%
16.1 17
 
2.1%
16 100
 
12.5%
15.6 416
52.1%
15.56 1
 
0.1%
15.3 1
 
0.1%
15 1
 
0.1%

ppi
Real number (ℝ)

Distinct50
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.0589
Minimum100
Maximum323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:37.814209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile127
Q1141
median141
Q3157
95-th percentile250
Maximum323
Range223
Interquartile range (IQR)16

Descriptive statistics

Standard deviation37.956163
Coefficient of variation (CV)0.23862961
Kurtosis3.181529
Mean159.0589
Median Absolute Deviation (MAD)1
Skewness1.9033281
Sum126929
Variance1440.6703
MonotonicityNot monotonic
2024-08-17T18:30:38.109865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141 370
46.4%
157 103
 
12.9%
142 37
 
4.6%
162 33
 
4.1%
112 21
 
2.6%
283 21
 
2.6%
243 20
 
2.5%
137 19
 
2.4%
189 17
 
2.1%
100 12
 
1.5%
Other values (40) 145
 
18.2%
ValueCountFrequency (%)
100 12
 
1.5%
112 21
 
2.6%
127 8
 
1.0%
134 1
 
0.1%
135 9
 
1.1%
137 19
 
2.4%
138 3
 
0.4%
140 4
 
0.5%
141 370
46.4%
142 37
 
4.6%
ValueCountFrequency (%)
323 1
 
0.1%
290 2
 
0.3%
283 21
2.6%
280 2
 
0.3%
266 2
 
0.3%
264 1
 
0.1%
263 1
 
0.1%
255 7
 
0.9%
254 2
 
0.3%
250 2
 
0.3%

aspect_ratio
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
16:9
659 
16:10
136 
3:2
 
3

Length

Max length5
Median length4
Mean length4.1666667
Min length3

Characters and Unicode

Total characters3325
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16:9
2nd row16:9
3rd row16:9
4th row16:9
5th row16:9

Common Values

ValueCountFrequency (%)
16:9 659
82.6%
16:10 136
 
17.0%
3:2 3
 
0.4%

Length

2024-08-17T18:30:38.535851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:38.960247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
16:9 659
82.6%
16:10 136
 
17.0%
3:2 3
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1 931
28.0%
: 798
24.0%
6 795
23.9%
9 659
19.8%
0 136
 
4.1%
3 3
 
0.1%
2 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3325
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 931
28.0%
: 798
24.0%
6 795
23.9%
9 659
19.8%
0 136
 
4.1%
3 3
 
0.1%
2 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3325
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 931
28.0%
: 798
24.0%
6 795
23.9%
9 659
19.8%
0 136
 
4.1%
3 3
 
0.1%
2 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3325
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 931
28.0%
: 798
24.0%
6 795
23.9%
9 659
19.8%
0 136
 
4.1%
3 3
 
0.1%
2 3
 
0.1%

antiglare
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1
671 
0
127 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 671
84.1%
0 127
 
15.9%

Length

2024-08-17T18:30:39.389917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:39.825290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 671
84.1%
0 127
 
15.9%

Most occurring characters

ValueCountFrequency (%)
1 671
84.1%
0 127
 
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 671
84.1%
0 127
 
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 671
84.1%
0 127
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 671
84.1%
0 127
 
15.9%

touch_screen
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
0
732 
1
 
66

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 732
91.7%
1 66
 
8.3%

Length

2024-08-17T18:30:40.149239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:40.523876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 732
91.7%
1 66
 
8.3%

Most occurring characters

ValueCountFrequency (%)
0 732
91.7%
1 66
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 732
91.7%
1 66
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 732
91.7%
1 66
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 732
91.7%
1 66
 
8.3%

ram
Real number (ℝ)

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.789474
Minimum4
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:40.884788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q18
median16
Q316
95-th percentile32
Maximum64
Range60
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.7673873
Coefficient of variation (CV)0.49076472
Kurtosis11.783063
Mean13.789474
Median Absolute Deviation (MAD)0
Skewness2.3522104
Sum11004
Variance45.79753
MonotonicityNot monotonic
2024-08-17T18:30:41.286951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
16 431
54.0%
8 298
37.3%
32 45
 
5.6%
4 20
 
2.5%
64 3
 
0.4%
12 1
 
0.1%
ValueCountFrequency (%)
4 20
 
2.5%
8 298
37.3%
12 1
 
0.1%
16 431
54.0%
32 45
 
5.6%
64 3
 
0.4%
ValueCountFrequency (%)
64 3
 
0.4%
32 45
 
5.6%
16 431
54.0%
12 1
 
0.1%
8 298
37.3%
4 20
 
2.5%

hdd
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
0
779 
1024
 
15
64
 
2
128
 
1
32
 
1

Length

Max length4
Median length1
Mean length1.0626566
Min length1

Characters and Unicode

Total characters848
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 779
97.6%
1024 15
 
1.9%
64 2
 
0.3%
128 1
 
0.1%
32 1
 
0.1%

Length

2024-08-17T18:30:41.745100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:42.158613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 779
97.6%
1024 15
 
1.9%
64 2
 
0.3%
128 1
 
0.1%
32 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 794
93.6%
2 17
 
2.0%
4 17
 
2.0%
1 16
 
1.9%
6 2
 
0.2%
8 1
 
0.1%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 794
93.6%
2 17
 
2.0%
4 17
 
2.0%
1 16
 
1.9%
6 2
 
0.2%
8 1
 
0.1%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 794
93.6%
2 17
 
2.0%
4 17
 
2.0%
1 16
 
1.9%
6 2
 
0.2%
8 1
 
0.1%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 794
93.6%
2 17
 
2.0%
4 17
 
2.0%
1 16
 
1.9%
6 2
 
0.2%
8 1
 
0.1%
3 1
 
0.1%

ssd
Real number (ℝ)

Distinct8
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean613.69424
Minimum0
Maximum4096
Zeros6
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:42.368091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile256
Q1512
median512
Q3512
95-th percentile1024
Maximum4096
Range4096
Interquartile range (IQR)0

Descriptive statistics

Standard deviation308.22133
Coefficient of variation (CV)0.50223925
Kurtosis24.462445
Mean613.69424
Median Absolute Deviation (MAD)0
Skewness3.3226427
Sum489728
Variance95000.388
MonotonicityNot monotonic
2024-08-17T18:30:42.586924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
512 567
71.1%
1024 158
 
19.8%
256 48
 
6.0%
2048 10
 
1.3%
0 6
 
0.8%
128 4
 
0.5%
64 4
 
0.5%
4096 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.8%
64 4
 
0.5%
128 4
 
0.5%
256 48
 
6.0%
512 567
71.1%
1024 158
 
19.8%
2048 10
 
1.3%
4096 1
 
0.1%
ValueCountFrequency (%)
4096 1
 
0.1%
2048 10
 
1.3%
1024 158
 
19.8%
512 567
71.1%
256 48
 
6.0%
128 4
 
0.5%
64 4
 
0.5%
0 6
 
0.8%

graphic
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
Integrated
372 
NVIDIA
299 
AMD
127 

Length

Max length10
Median length6
Mean length7.387218
Min length3

Characters and Unicode

Total characters5895
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIntegrated
2nd rowIntegrated
3rd rowIntegrated
4th rowNVIDIA
5th rowIntegrated

Common Values

ValueCountFrequency (%)
Integrated 372
46.6%
NVIDIA 299
37.5%
AMD 127
 
15.9%

Length

2024-08-17T18:30:42.856620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:43.136015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
integrated 372
46.6%
nvidia 299
37.5%
amd 127
 
15.9%

Most occurring characters

ValueCountFrequency (%)
I 970
16.5%
t 744
12.6%
e 744
12.6%
D 426
7.2%
A 426
7.2%
n 372
 
6.3%
g 372
 
6.3%
r 372
 
6.3%
a 372
 
6.3%
d 372
 
6.3%
Other values (3) 725
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 970
16.5%
t 744
12.6%
e 744
12.6%
D 426
7.2%
A 426
7.2%
n 372
 
6.3%
g 372
 
6.3%
r 372
 
6.3%
a 372
 
6.3%
d 372
 
6.3%
Other values (3) 725
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 970
16.5%
t 744
12.6%
e 744
12.6%
D 426
7.2%
A 426
7.2%
n 372
 
6.3%
g 372
 
6.3%
r 372
 
6.3%
a 372
 
6.3%
d 372
 
6.3%
Other values (3) 725
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 970
16.5%
t 744
12.6%
e 744
12.6%
D 426
7.2%
A 426
7.2%
n 372
 
6.3%
g 372
 
6.3%
r 372
 
6.3%
a 372
 
6.3%
d 372
 
6.3%
Other values (3) 725
12.3%

cache
Categorical

MISSING 

Distinct16
Distinct (%)2.1%
Missing27
Missing (%)3.4%
Memory size6.4 KiB
12
235 
16
126 
24
97 
4
72 
8
63 
Other values (11)
178 

Length

Max length17
Median length2
Mean length1.7808042
Min length1

Characters and Unicode

Total characters1373
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.4%

Sample

1st row6
2nd row8
3rd row12
4th row12
5th row8

Common Values

ValueCountFrequency (%)
12 235
29.4%
16 126
15.8%
24 97
12.2%
4 72
 
9.0%
8 63
 
7.9%
18 59
 
7.4%
6 47
 
5.9%
10 38
 
4.8%
36 13
 
1.6%
30 9
 
1.1%
Other values (6) 12
 
1.5%
(Missing) 27
 
3.4%

Length

2024-08-17T18:30:43.368518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12 235
30.4%
16 126
16.3%
24 97
12.5%
4 72
 
9.3%
8 63
 
8.2%
18 59
 
7.6%
6 47
 
6.1%
10 38
 
4.9%
36 13
 
1.7%
30 9
 
1.2%
Other values (7) 14
 
1.8%

Most occurring characters

ValueCountFrequency (%)
1 458
33.4%
2 339
24.7%
6 189
13.8%
4 172
 
12.5%
8 122
 
8.9%
0 51
 
3.7%
3 24
 
1.7%
a 3
 
0.2%
2
 
0.1%
h 2
 
0.1%
Other values (8) 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1373
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 458
33.4%
2 339
24.7%
6 189
13.8%
4 172
 
12.5%
8 122
 
8.9%
0 51
 
3.7%
3 24
 
1.7%
a 3
 
0.2%
2
 
0.1%
h 2
 
0.1%
Other values (8) 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1373
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 458
33.4%
2 339
24.7%
6 189
13.8%
4 172
 
12.5%
8 122
 
8.9%
0 51
 
3.7%
3 24
 
1.7%
a 3
 
0.2%
2
 
0.1%
h 2
 
0.1%
Other values (8) 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1373
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 458
33.4%
2 339
24.7%
6 189
13.8%
4 172
 
12.5%
8 122
 
8.9%
0 51
 
3.7%
3 24
 
1.7%
a 3
 
0.2%
2
 
0.1%
h 2
 
0.1%
Other values (8) 11
 
0.8%

thread
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)1.2%
Missing20
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean12.478149
Minimum2
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:43.597947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median12
Q316
95-th percentile20
Maximum32
Range30
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.2556434
Coefficient of variation (CV)0.42118773
Kurtosis1.9502163
Mean12.478149
Median Absolute Deviation (MAD)4
Skewness0.63520005
Sum9708
Variance27.621787
MonotonicityNot monotonic
2024-08-17T18:30:43.786784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
12 284
35.6%
16 191
23.9%
8 143
17.9%
20 59
 
7.4%
4 48
 
6.0%
2 27
 
3.4%
32 13
 
1.6%
24 10
 
1.3%
6 3
 
0.4%
(Missing) 20
 
2.5%
ValueCountFrequency (%)
2 27
 
3.4%
4 48
 
6.0%
6 3
 
0.4%
8 143
17.9%
12 284
35.6%
16 191
23.9%
20 59
 
7.4%
24 10
 
1.3%
32 13
 
1.6%
ValueCountFrequency (%)
32 13
 
1.6%
24 10
 
1.3%
20 59
 
7.4%
16 191
23.9%
12 284
35.6%
8 143
17.9%
6 3
 
0.4%
4 48
 
6.0%
2 27
 
3.4%

core
Real number (ℝ)

Distinct10
Distinct (%)1.3%
Missing4
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean8.2380353
Minimum2
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:44.002431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q16
median8
Q310
95-th percentile14
Maximum24
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.0442089
Coefficient of variation (CV)0.49091911
Kurtosis2.086057
Mean8.2380353
Median Absolute Deviation (MAD)2
Skewness0.90330937
Sum6541
Variance16.355626
MonotonicityNot monotonic
2024-08-17T18:30:44.216321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 156
19.5%
8 149
18.7%
10 145
18.2%
4 95
11.9%
12 88
11.0%
2 71
8.9%
14 63
7.9%
24 13
 
1.6%
16 11
 
1.4%
5 3
 
0.4%
(Missing) 4
 
0.5%
ValueCountFrequency (%)
2 71
8.9%
4 95
11.9%
5 3
 
0.4%
6 156
19.5%
8 149
18.7%
10 145
18.2%
12 88
11.0%
14 63
7.9%
16 11
 
1.4%
24 13
 
1.6%
ValueCountFrequency (%)
24 13
 
1.6%
16 11
 
1.4%
14 63
7.9%
12 88
11.0%
10 145
18.2%
8 149
18.7%
6 156
19.5%
5 3
 
0.4%
4 95
11.9%
2 71
8.9%

hdmi
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1
753 
0
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 753
94.4%
0 45
 
5.6%

Length

2024-08-17T18:30:44.432846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:44.692260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 753
94.4%
0 45
 
5.6%

Most occurring characters

ValueCountFrequency (%)
1 753
94.4%
0 45
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 753
94.4%
0 45
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 753
94.4%
0 45
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 753
94.4%
0 45
 
5.6%

mcr
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
0
558 
1
240 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 558
69.9%
1 240
30.1%

Length

2024-08-17T18:30:44.896719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:45.137505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 558
69.9%
1 240
30.1%

Most occurring characters

ValueCountFrequency (%)
0 558
69.9%
1 240
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 558
69.9%
1 240
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 558
69.9%
1 240
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 558
69.9%
1 240
30.1%

wifi
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1
795 
0
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 795
99.6%
0 3
 
0.4%

Length

2024-08-17T18:30:45.338640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:45.582795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 795
99.6%
0 3
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1 795
99.6%
0 3
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 795
99.6%
0 3
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 795
99.6%
0 3
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 795
99.6%
0 3
 
0.4%

bluetooth
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1
798 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 798
100.0%

Length

2024-08-17T18:30:45.778966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:46.026161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 798
100.0%

Most occurring characters

ValueCountFrequency (%)
1 798
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 798
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 798
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 798
100.0%

backlit_keyboard
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1
640 
0
158 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 640
80.2%
0 158
 
19.8%

Length

2024-08-17T18:30:46.212362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:46.450093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 640
80.2%
0 158
 
19.8%

Most occurring characters

ValueCountFrequency (%)
1 640
80.2%
0 158
 
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 640
80.2%
0 158
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 640
80.2%
0 158
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 640
80.2%
0 158
 
19.8%

inbuilt_microphone
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1
788 
0
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 788
98.7%
0 10
 
1.3%

Length

2024-08-17T18:30:46.727645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:46.988185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 788
98.7%
0 10
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 788
98.7%
0 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 788
98.7%
0 10
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 788
98.7%
0 10
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 788
98.7%
0 10
 
1.3%

thunderbolt
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
0
579 
1
219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579
72.6%
1 219
 
27.4%

Length

2024-08-17T18:30:47.185224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:47.421948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 579
72.6%
1 219
 
27.4%

Most occurring characters

ValueCountFrequency (%)
0 579
72.6%
1 219
 
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 579
72.6%
1 219
 
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 579
72.6%
1 219
 
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 579
72.6%
1 219
 
27.4%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
0
552 
1
246 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 552
69.2%
1 246
30.8%

Length

2024-08-17T18:30:47.633981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:47.882833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 552
69.2%
1 246
30.8%

Most occurring characters

ValueCountFrequency (%)
0 552
69.2%
1 246
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 552
69.2%
1 246
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 552
69.2%
1 246
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 552
69.2%
1 246
30.8%

ethernet
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
0
456 
1
342 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 456
57.1%
1 342
42.9%

Length

2024-08-17T18:30:48.081371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:48.804632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 456
57.1%
1 342
42.9%

Most occurring characters

ValueCountFrequency (%)
0 456
57.1%
1 342
42.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 456
57.1%
1 342
42.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 456
57.1%
1 342
42.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 456
57.1%
1 342
42.9%

display_port
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
0
783 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 783
98.1%
1 15
 
1.9%

Length

2024-08-17T18:30:49.008411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:49.247212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 783
98.1%
1 15
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 783
98.1%
1 15
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 783
98.1%
1 15
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 783
98.1%
1 15
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 783
98.1%
1 15
 
1.9%

camera
Categorical

MISSING 

Distinct5
Distinct (%)17.2%
Missing769
Missing (%)96.4%
Memory size6.4 KiB
5.0
15 
2.0
1.0
0.3
720.0
 
1

Length

Max length5
Median length3
Mean length3.0689655
Min length3

Characters and Unicode

Total characters89
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.4%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 15
 
1.9%
2.0 7
 
0.9%
1.0 4
 
0.5%
0.3 2
 
0.3%
720.0 1
 
0.1%
(Missing) 769
96.4%

Length

2024-08-17T18:30:49.478102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:49.773779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0 15
51.7%
2.0 7
24.1%
1.0 4
 
13.8%
0.3 2
 
6.9%
720.0 1
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 30
33.7%
. 29
32.6%
5 15
16.9%
2 8
 
9.0%
1 4
 
4.5%
3 2
 
2.2%
7 1
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 30
33.7%
. 29
32.6%
5 15
16.9%
2 8
 
9.0%
1 4
 
4.5%
3 2
 
2.2%
7 1
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 30
33.7%
. 29
32.6%
5 15
16.9%
2 8
 
9.0%
1 4
 
4.5%
3 2
 
2.2%
7 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 30
33.7%
. 29
32.6%
5 15
16.9%
2 8
 
9.0%
1 4
 
4.5%
3 2
 
2.2%
7 1
 
1.1%

num_of_cell
Categorical

MISSING 

Distinct4
Distinct (%)0.6%
Missing159
Missing (%)19.9%
Memory size6.4 KiB
3.0
413 
4.0
154 
6.0
44 
2.0
 
28

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1917
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 413
51.8%
4.0 154
 
19.3%
6.0 44
 
5.5%
2.0 28
 
3.5%
(Missing) 159
 
19.9%

Length

2024-08-17T18:30:50.024851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:50.311720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 413
64.6%
4.0 154
 
24.1%
6.0 44
 
6.9%
2.0 28
 
4.4%

Most occurring characters

ValueCountFrequency (%)
. 639
33.3%
0 639
33.3%
3 413
21.5%
4 154
 
8.0%
6 44
 
2.3%
2 28
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 639
33.3%
0 639
33.3%
3 413
21.5%
4 154
 
8.0%
6 44
 
2.3%
2 28
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 639
33.3%
0 639
33.3%
3 413
21.5%
4 154
 
8.0%
6 44
 
2.3%
2 28
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 639
33.3%
0 639
33.3%
3 413
21.5%
4 154
 
8.0%
6 44
 
2.3%
2 28
 
1.5%

battery_capacity
Real number (ℝ)

MISSING 

Distinct59
Distinct (%)9.9%
Missing202
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean57.002936
Minimum6
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:50.556126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile39.825
Q145
median52.5
Q368.575
95-th percentile90
Maximum100
Range94
Interquartile range (IQR)23.575

Descriptive statistics

Standard deviation17.536492
Coefficient of variation (CV)0.30764191
Kurtosis0.10937653
Mean57.002936
Median Absolute Deviation (MAD)10.5
Skewness0.80549918
Sum33973.75
Variance307.52857
MonotonicityNot monotonic
2024-08-17T18:30:50.860772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 79
 
9.9%
50 62
 
7.8%
45 38
 
4.8%
90 37
 
4.6%
70 35
 
4.4%
53.5 29
 
3.6%
52.5 28
 
3.5%
54 22
 
2.8%
42 19
 
2.4%
51 17
 
2.1%
Other values (49) 230
28.8%
(Missing) 202
25.3%
ValueCountFrequency (%)
6 1
 
0.1%
17.85 5
 
0.6%
18.5 3
 
0.4%
36 6
 
0.8%
37 7
 
0.9%
38 7
 
0.9%
39.3 1
 
0.1%
40 9
 
1.1%
41 79
9.9%
42 19
 
2.4%
ValueCountFrequency (%)
100 1
 
0.1%
99.9 6
 
0.8%
99 9
 
1.1%
97 7
 
0.9%
96 1
 
0.1%
94.3 1
 
0.1%
90 37
4.6%
87 1
 
0.1%
86 13
 
1.6%
84 1
 
0.1%

pixel_width
Real number (ℝ)

Distinct18
Distinct (%)2.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2040.9084
Minimum1080
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:51.112288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1080
5-th percentile1366
Q11920
median1920
Q31920
95-th percentile2880
Maximum3840
Range2760
Interquartile range (IQR)0

Descriptive statistics

Standard deviation442.0295
Coefficient of variation (CV)0.21658468
Kurtosis5.4209197
Mean2040.9084
Median Absolute Deviation (MAD)0
Skewness2.0450953
Sum1626604
Variance195390.08
MonotonicityNot monotonic
2024-08-17T18:30:51.350755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1920 616
77.2%
2560 57
 
7.1%
1366 38
 
4.8%
2880 31
 
3.9%
3840 17
 
2.1%
3200 12
 
1.5%
1600 6
 
0.8%
1080 5
 
0.6%
1200 3
 
0.4%
3456 3
 
0.4%
Other values (8) 9
 
1.1%
ValueCountFrequency (%)
1080 5
 
0.6%
1200 3
 
0.4%
1366 38
 
4.8%
1440 1
 
0.1%
1600 6
 
0.8%
1920 616
77.2%
2160 1
 
0.1%
2240 1
 
0.1%
2520 1
 
0.1%
2560 57
 
7.1%
ValueCountFrequency (%)
3840 17
 
2.1%
3480 1
 
0.1%
3456 3
 
0.4%
3200 12
 
1.5%
3120 1
 
0.1%
3072 2
 
0.3%
3024 1
 
0.1%
2880 31
3.9%
2560 57
7.1%
2520 1
 
0.1%

pixel_height
Real number (ℝ)

Distinct21
Distinct (%)2.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1216.7503
Minimum768
Maximum2560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-08-17T18:30:51.640316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum768
5-th percentile1080
Q11080
median1080
Q31200
95-th percentile1920
Maximum2560
Range1792
Interquartile range (IQR)120

Descriptive statistics

Standard deviation330.64643
Coefficient of variation (CV)0.2717455
Kurtosis4.4617572
Mean1216.7503
Median Absolute Deviation (MAD)0
Skewness2.1076996
Sum969750
Variance109327.06
MonotonicityNot monotonic
2024-08-17T18:30:52.072957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1080 516
64.7%
1200 98
 
12.3%
1600 44
 
5.5%
768 38
 
4.8%
1800 30
 
3.8%
2400 15
 
1.9%
1440 12
 
1.5%
1920 10
 
1.3%
2560 7
 
0.9%
2000 7
 
0.9%
Other values (11) 20
 
2.5%
ValueCountFrequency (%)
768 38
 
4.8%
1080 516
64.7%
1200 98
 
12.3%
1280 1
 
0.1%
1400 1
 
0.1%
1440 12
 
1.5%
1600 44
 
5.5%
1620 5
 
0.6%
1660 1
 
0.1%
1664 1
 
0.1%
ValueCountFrequency (%)
2560 7
 
0.9%
2400 15
1.9%
2234 1
 
0.1%
2160 6
 
0.8%
2080 1
 
0.1%
2000 7
 
0.9%
1964 1
 
0.1%
1920 10
 
1.3%
1864 1
 
0.1%
1800 30
3.8%

usb3
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2
345 
0
283 
3
108 
1
60 
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row3
4th row3
5th row0

Common Values

ValueCountFrequency (%)
2 345
43.2%
0 283
35.5%
3 108
 
13.5%
1 60
 
7.5%
4 2
 
0.3%

Length

2024-08-17T18:30:52.587655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:53.124575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 345
43.2%
0 283
35.5%
3 108
 
13.5%
1 60
 
7.5%
4 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 345
43.2%
0 283
35.5%
3 108
 
13.5%
1 60
 
7.5%
4 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 345
43.2%
0 283
35.5%
3 108
 
13.5%
1 60
 
7.5%
4 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 345
43.2%
0 283
35.5%
3 108
 
13.5%
1 60
 
7.5%
4 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 345
43.2%
0 283
35.5%
3 108
 
13.5%
1 60
 
7.5%
4 2
 
0.3%

usb2
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
0
561 
1
195 
2
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 561
70.3%
1 195
 
24.4%
2 42
 
5.3%

Length

2024-08-17T18:30:53.561307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:54.052368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 561
70.3%
1 195
 
24.4%
2 42
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 561
70.3%
1 195
 
24.4%
2 42
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 561
70.3%
1 195
 
24.4%
2 42
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 561
70.3%
1 195
 
24.4%
2 42
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 561
70.3%
1 195
 
24.4%
2 42
 
5.3%

type_c
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
0
758 
1
 
16
2
 
13
3
 
8
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters798
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 758
95.0%
1 16
 
2.0%
2 13
 
1.6%
3 8
 
1.0%
4 3
 
0.4%

Length

2024-08-17T18:30:54.449409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:54.927938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 758
95.0%
1 16
 
2.0%
2 13
 
1.6%
3 8
 
1.0%
4 3
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 758
95.0%
1 16
 
2.0%
2 13
 
1.6%
3 8
 
1.0%
4 3
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 758
95.0%
1 16
 
2.0%
2 13
 
1.6%
3 8
 
1.0%
4 3
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 758
95.0%
1 16
 
2.0%
2 13
 
1.6%
3 8
 
1.0%
4 3
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 758
95.0%
1 16
 
2.0%
2 13
 
1.6%
3 8
 
1.0%
4 3
 
0.4%

processor_brand
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
Intel
581 
AMD
207 
Apple
 
8
Mediatek
 
2

Length

Max length8
Median length5
Mean length4.4887218
Min length3

Characters and Unicode

Total characters3582
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIntel
2nd rowIntel
3rd rowIntel
4th rowIntel
5th rowAMD

Common Values

ValueCountFrequency (%)
Intel 581
72.8%
AMD 207
 
25.9%
Apple 8
 
1.0%
Mediatek 2
 
0.3%

Length

2024-08-17T18:30:55.432619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-17T18:30:55.913733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
intel 581
72.8%
amd 207
 
25.9%
apple 8
 
1.0%
mediatek 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 593
16.6%
l 589
16.4%
t 583
16.3%
I 581
16.2%
n 581
16.2%
A 215
 
6.0%
M 209
 
5.8%
D 207
 
5.8%
p 16
 
0.4%
d 2
 
0.1%
Other values (3) 6
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3582
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 593
16.6%
l 589
16.4%
t 583
16.3%
I 581
16.2%
n 581
16.2%
A 215
 
6.0%
M 209
 
5.8%
D 207
 
5.8%
p 16
 
0.4%
d 2
 
0.1%
Other values (3) 6
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3582
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 593
16.6%
l 589
16.4%
t 583
16.3%
I 581
16.2%
n 581
16.2%
A 215
 
6.0%
M 209
 
5.8%
D 207
 
5.8%
p 16
 
0.4%
d 2
 
0.1%
Other values (3) 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3582
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 593
16.6%
l 589
16.4%
t 583
16.3%
I 581
16.2%
n 581
16.2%
A 215
 
6.0%
M 209
 
5.8%
D 207
 
5.8%
p 16
 
0.4%
d 2
 
0.1%
Other values (3) 6
 
0.2%

processor_model
Categorical

Distinct22
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
i5
276 
i7
144 
5
107 
i3
91 
7
57 
Other values (17)
123 

Length

Max length7
Median length2
Mean length1.914787
Min length1

Characters and Unicode

Total characters1528
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.6%

Sample

1st rowi3
2nd rowi5
3rd rowi5
4th rowi5
5th row5

Common Values

ValueCountFrequency (%)
i5 276
34.6%
i7 144
18.0%
5 107
 
13.4%
i3 91
 
11.4%
7 57
 
7.1%
i9 38
 
4.8%
3 21
 
2.6%
N4020 13
 
1.6%
9 12
 
1.5%
N4500 8
 
1.0%
Other values (12) 31
 
3.9%

Length

2024-08-17T18:30:56.360669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i5 276
34.6%
i7 144
18.0%
5 107
 
13.4%
i3 91
 
11.4%
7 57
 
7.1%
i9 38
 
4.8%
3 21
 
2.6%
n4020 13
 
1.6%
9 12
 
1.5%
n4500 8
 
1.0%
Other values (12) 31
 
3.9%

Most occurring characters

ValueCountFrequency (%)
i 549
35.9%
5 408
26.7%
7 206
 
13.5%
3 125
 
8.2%
0 73
 
4.8%
9 51
 
3.3%
N 32
 
2.1%
4 25
 
1.6%
2 22
 
1.4%
M 10
 
0.7%
Other values (9) 27
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 549
35.9%
5 408
26.7%
7 206
 
13.5%
3 125
 
8.2%
0 73
 
4.8%
9 51
 
3.3%
N 32
 
2.1%
4 25
 
1.6%
2 22
 
1.4%
M 10
 
0.7%
Other values (9) 27
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 549
35.9%
5 408
26.7%
7 206
 
13.5%
3 125
 
8.2%
0 73
 
4.8%
9 51
 
3.3%
N 32
 
2.1%
4 25
 
1.6%
2 22
 
1.4%
M 10
 
0.7%
Other values (9) 27
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 549
35.9%
5 408
26.7%
7 206
 
13.5%
3 125
 
8.2%
0 73
 
4.8%
9 51
 
3.3%
N 32
 
2.1%
4 25
 
1.6%
2 22
 
1.4%
M 10
 
0.7%
Other values (9) 27
 
1.8%

processor_gen
Categorical

Distinct16
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
12th
215 
13th
212 
11th
109 
7th
97 
5th
75 
Other values (11)
90 

Length

Max length8
Median length4
Mean length3.7882206
Min length3

Characters and Unicode

Total characters3023
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row11th
2nd row11th
3rd row12th
4th row13th
5th row5th

Common Values

ValueCountFrequency (%)
12th 215
26.9%
13th 212
26.6%
11th 109
13.7%
7th 97
12.2%
5th 75
 
9.4%
Intel 27
 
3.4%
6th 14
 
1.8%
3rd 14
 
1.8%
10th 11
 
1.4%
Apple 8
 
1.0%
Other values (6) 16
 
2.0%

Length

2024-08-17T18:30:56.849694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12th 215
26.9%
13th 212
26.6%
11th 109
13.7%
7th 97
12.2%
5th 75
 
9.4%
intel 28
 
3.5%
6th 14
 
1.8%
3rd 14
 
1.8%
10th 11
 
1.4%
apple 8
 
1.0%
Other values (5) 15
 
1.9%

Most occurring characters

ValueCountFrequency (%)
t 769
25.4%
h 741
24.5%
1 656
21.7%
3 226
 
7.5%
2 215
 
7.1%
7 97
 
3.2%
5 75
 
2.5%
e 40
 
1.3%
l 36
 
1.2%
n 28
 
0.9%
Other values (16) 140
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3023
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 769
25.4%
h 741
24.5%
1 656
21.7%
3 226
 
7.5%
2 215
 
7.1%
7 97
 
3.2%
5 75
 
2.5%
e 40
 
1.3%
l 36
 
1.2%
n 28
 
0.9%
Other values (16) 140
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3023
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 769
25.4%
h 741
24.5%
1 656
21.7%
3 226
 
7.5%
2 215
 
7.1%
7 97
 
3.2%
5 75
 
2.5%
e 40
 
1.3%
l 36
 
1.2%
n 28
 
0.9%
Other values (16) 140
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3023
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 769
25.4%
h 741
24.5%
1 656
21.7%
3 226
 
7.5%
2 215
 
7.1%
7 97
 
3.2%
5 75
 
2.5%
e 40
 
1.3%
l 36
 
1.2%
n 28
 
0.9%
Other values (16) 140
 
4.6%

Interactions

2024-08-17T18:30:23.752120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:32.800627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:36.645749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:40.326993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:44.449076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:48.783178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:52.471015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:55.999688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:00.489798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:04.782197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:08.200370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:12.735236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:17.023950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:20.331892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:23.994261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:33.233699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:36.892108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:40.630829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:44.834051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:49.048273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:52.726901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:56.233446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:00.764025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:05.015037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:08.439862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:13.042628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:17.253928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:20.588601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:24.250208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:33.576545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:37.149332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:40.889948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:45.200422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:49.294960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:52.987833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:56.492646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:01.276144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:05.287806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:08.694074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:13.443895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:17.533936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:20.858977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:24.513649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:33.813934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:37.419464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:41.147445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:45.575831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:49.545648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:53.258261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:56.736940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:01.528536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:05.535007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:08.962464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:13.823021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:17.760755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:21.099472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:24.879947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:34.067134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:37.676545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:41.399849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:45.987995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:49.780337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:53.508943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:57.024004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:01.791804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:05.805728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:09.211561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:14.188571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:17.998046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:21.341762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:25.224295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:34.313229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:37.921597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:41.845576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:46.378634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:50.018740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:53.764586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:57.413460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:02.041579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:06.039754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:09.473186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:14.530547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:18.231735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:21.595199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:25.631856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:34.569489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:38.198895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:42.087711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:46.750327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:50.277353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:54.019894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:57.713607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:02.284930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:06.294659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:09.732995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:14.775753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:18.483924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:21.838553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:26.020440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:34.805998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:38.482515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:42.324480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:47.074824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:50.732939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:54.270375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:58.036014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:02.740802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:06.536114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:10.122024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:15.327494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:18.704129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:22.069884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:26.413337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:35.045171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:38.740544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:42.563437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:47.310050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:50.966396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:54.517524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:58.341128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:02.977254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:06.772645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:10.355125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:15.576249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:18.931060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:22.301818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:26.776803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:35.272546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:38.979233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:42.823226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:47.560519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:51.233471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:54.747264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:58.688588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:03.201095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:07.007755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:10.585577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:15.805971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:19.191846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:22.550586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:27.125283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:35.678610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:39.226523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:43.063696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:47.812791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:51.492637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:54.994341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:59.018970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:03.664844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:07.235140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:11.064537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:16.049295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:19.439864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:22.783020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:27.489615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:35.909529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:39.493637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:43.369271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:48.064594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:51.726124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:55.248307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:59.340175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:03.894858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:07.470029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:11.442350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:16.286786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:19.660034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:23.014527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:27.857278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:36.132474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:39.734783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:43.715737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:48.285704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:51.959535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:55.498423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:59.724640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:04.112147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:07.702432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:12.045644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:16.528412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:19.868560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:23.243984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:28.191586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:36.369935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:39.990144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:44.045347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:48.538879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:52.209083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:29:55.745791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:00.095258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:04.518725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:07.949109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:12.405918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:16.776908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:20.093450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-17T18:30:23.476979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-08-17T18:30:28.676019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-17T18:30:29.747597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-17T18:30:30.777285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

namepricebrandno_of_votesratingosutilitythicknessweightwarrantydisplay_sizeppiaspect_ratioantiglaretouch_screenramhddssdgraphiccachethreadcorehdmimcrwifibluetoothbacklit_keyboardinbuilt_microphonethunderboltfingerprint_sensorethernetdisplay_portcameranum_of_cellbattery_capacitypixel_widthpixel_heightusb3usb2type_cprocessor_brandprocessor_modelprocessor_gen
0Acer One 14 Z8-415 Laptop (11th Gen Core i3 / 8GB/ 512GB SSD/ Win11 Home)25990.0Acer10864.10WindowsEveryday Use21.601.501.014.015716:91080512Integrated64.02.01011010010NaN2.045.501920.01080.0010Inteli311th
1Wings Nuvobook V1 Laptop (11th Gen Core i5/ 8GB/ 512GB SSD/ Win11)34990.0Wings694.70WindowsBusinessNaN1.601.015.614116:91080512Integrated88.04.01011110000NaNNaN17.851080.01920.0010Inteli511th
2MSI Thin GF63 12HW-012IN Gaming Laptop (12th Gen Core i5/ 16GB/ 512GB SSD/ Win11 Home/ 4GB Graphics)49990.0MSI1724.25WindowsPerformance21.701.862.015.614116:910160512Integrated1212.08.01011111010NaN3.051.001920.01080.0300Inteli512th
3Acer Nitro V ANV15-51 Gaming Laptop (13th Gen Core i5/ 8GB/ 512GB SSD/ Win11/ 6GB Graph)79745.0Acer754.50WindowsGaming25.902.601.015.614116:91080512NVIDIA1212.08.01011110010NaNNaNNaN1920.01080.0300Inteli513th
4Acer Aspire Lite AL15-51 Laptop (AMD Ryzen 5 5500U/ 16GB/ 512GB SSD/ Win11)35990.0Acer1624.00WindowsPerformance19.701.591.015.614116:910160512Integrated812.06.01111010010NaN3.036.001920.01080.0010AMD55th
5HP Victus 16-s0094AX Gaming Laptop (AMD Ryzen 7 7840HS/ 16GB/ 1TB SSD/ Win11/ 6GB Graph)92221.0HP4064.30WindowsGaming24.002.481.016.113716:9101601024NVIDIA1616.08.01011111010NaN4.070.001920.01080.0300AMD77th
6Wings Nuvobook Pro Laptop (11th Gen Core i7/ 16GB/ 512GB SSD/ Win11)45990.0Wings894.15WindowsBusiness16.301.481.014.015716:910160512Integrated128.04.01011110000NaNNaN17.851080.01920.0010Inteli711th
7HP 15s-fr2515TU Laptop (11th Gen Core i3/ 8GB/ 512GB SSD/ Win11)37650.0HP3924.10WindowsEveryday Use17.901.701.015.614116:91080512Integrated64.02.01111010000NaN3.041.001920.01080.0200Inteli311th
8Asus Vivobook 16X 2022 M1603QA-MB502WS Laptop (Ryzen 5-5600H/ 8GB/ 512GB SSD/ Win11 Home)49990.0Asus7684.25WindowsGaming20.001.801.016.028316:101080512AMD1612.06.01011110100NaN3.050.001920.01200.0010AMD55th
9MSI Modern 14 C11M-031IN Laptop (11th Gen Core i3/ 8GB/ 512GB SSD/ Win11 Home)28990.0MSI7034.20WindowsPerformance19.351.401.014.015716:91080512Integrated64.02.01011110000NaN3.0NaN1920.01080.0020Inteli311th
namepricebrandno_of_votesratingosutilitythicknessweightwarrantydisplay_sizeppiaspect_ratioantiglaretouch_screenramhddssdgraphiccachethreadcorehdmimcrwifibluetoothbacklit_keyboardinbuilt_microphonethunderboltfingerprint_sensorethernetdisplay_portcameranum_of_cellbattery_capacitypixel_widthpixel_heightusb3usb2type_cprocessor_brandprocessor_modelprocessor_gen
788Asus VivoBook 14 2023 X1404VA-NK541WS Laptop (13th Gen Core i5/ 16GB/ 512GB SSD/ Win11 Home)63990.0Asus854.30WindowsPerformanceNaN1.401.014.015716:910160512Integrated1212.010.01011010100NaN3.0NaN1920.01080.0010Inteli513th
789Dell Inspiron 3525 D560771WIN9S Laptop (AMD Ryzen 5 5625U/ 8GB/ 512GB SSD/ Win11)45999.0Dell1094.50WindowsEveryday Use23.51.681.015.614116:91080512AMD1612.06.01111110010NaN3.041.01920.01080.0010AMD55th
790Asus VivoBook 14 2023 X1404VA-NK522WS Laptop (13th Gen Core i5/ 8GB/ 512GB SSD/ Win11 Home)59990.0Asus1304.20WindowsPerformanceNaN1.401.014.015716:91080512Integrated1212.010.01011110100NaN3.0NaN1920.01080.0010Inteli513th
791Asus Vivobook 15 OLED 2023 X1505VA-LK542WS Laptop (13th Gen Core i5/ 16GB/ 512GB SSD/ Win11 Home)74990.0Asus854.25WindowsPerformanceNaN1.701.015.614116:910160512Integrated1816.012.01011110100NaNNaN50.01920.01080.0010Inteli513th
792Asus VivoBook 14 2023 X1404VA-NK321WS Laptop (13th Gen Core i3/ 8GB/ 512GB SSD/ Win11 Home)45990.0Asus1484.55WindowsPerformanceNaN1.401.014.015716:91080512IntegratedNaN8.06.01011110100NaN3.0NaN1920.01080.0010Inteli313th
793Asus Zenbook S13 OLED 2023 UX5304VA-NQ762WS Laptop (13th Gen Core i7/ 32GB/ 1TB SSD/ Win11)144990.0Asus1004.30WindowsPerformance10.91.001.013.325516:10103201024Integrated1212.010.01011111000NaN4.063.02880.01800.0100Inteli713th
794Asus Zenbook 14 OLED 2023 UX3402VA-KM541WS Laptop (13th Gen Core i5/ 16GB/ 512GB SSD/ Win11 Home)92990.0Asus824.15WindowsPerformance16.91.391.014.024316:1010160512Integrated1216.012.01011110100NaNNaN75.02880.01800.0100Inteli513th
795Asus Zenbook 14 OLED 2023 UX3402VA-KM742WS Laptop (13th Gen Core i7/ 16GB/ 512GB SSD/ Win11 Home)112990.0Asus804.60WindowsPerformance16.91.391.014.024316:1010160512Integrated1816.012.01011110100NaNNaN75.02880.01800.0100Inteli713th
796Asus Zenbook S13 OLED 2023 UX5304VA-NQ542WS Laptop (13th Gen Core i5/ 16GB/ 512GB SSD/ Win11)99990.0Asus1114.45WindowsPerformance10.91.001.013.325516:1010160512Integrated1212.010.01011111000NaN4.063.02880.01800.0100Inteli513th
797Asus Vivobook S14 Flip 2022 TN3402QA-LZ520WS Laptop (AMD Ryzen 5-5600H/ 8GB/ 512GB SSD/Win11)58990.0Asus2054.50WindowsPerformanceNaN1.501.014.016216:100180512AMD1612.06.01011110100NaN3.0NaN1920.01200.0010AMD55th